Pathology is a medical field in which whether an abnormality is present is determined by examining a tissue sample with the naked eye or a microscope and then analyzing the results of the examination. For example, in order to diagnose cancer, a pathologist makes a diagnosis of cancer by examining a tissue sample of a corresponding suspected tissue via a microscope and then determining whether a cancer cell is present. This is referred to as pathologic diagnosis. This pathologic diagnosis is the procedure of confirming a diagnosis of a suspected lesion of a patient, and may be viewed as the final step of diagnosis.
In order to automatically perform pathologic diagnosis using equipment such as a computer or the like, there are required images and pathologic diagnosis data in which existing images, used to be compared with an input query image and to perform analysis, and corresponding results of pathologic diagnosis have been organized into a database.
The database to be compared and analyzed needs to store normal and abnormal medical images, and the medical images each having information about the presence or absence of a lesion, the result of pathologic diagnosis of the lesion, and the location of the corresponding lesion. And an input query image are compared with the medical images stored in the database and analyzed. The information about the presence or absence of a lesion, the result of pathologic diagnosis of the lesion, and the location of the lesion are referred to as tag information. The database has higher reliability in proportion to the number of medical images including such tag information. In particular, if information optimized for prediction is always maintained by training based on a massive amount of images having such tag information using a technology such as machine learning, more accurate results can be predicted.
However, in a medical imaging field, there has not been proposed a technology that can predict the result of pathologic diagnosis of a lesion by efficiently performing the analysis and diagnosis of a medical image using a learning technology such as machine learning or the like.
Furthermore, there are many cases in which various types of medical images used in existing medical institutions have not been put into databases or have not been organized. In particular, for most cases, even when an image itself is present, there is no information about the presence or absence of a lesion in the corresponding image, the result of pathologic diagnosis of the lesion, or the location of the lesion, i.e., no tag information. Furthermore, there are many cases in which even when tag information is present, the location information of a lesion is not present and tag information indicative of only the presence or absence of the lesion or the result of pathologic diagnosis of the corresponding lesion is present. Accordingly, there are many difficulties in constructing a reliable database.